Oxygen levels in vivo are autonomously regulated by a supply-demand balance, which can be altered in disease states. However, the oxygen levels of in vitro cell culture systems, particularly microscale cell culture, are typically dominated by either supply or demand. Further, the oxygen microenvironment in these systems is rarely monitored or reported. Here, a method to establish and dynamically monitor autonomously regulated oxygen microenvironments (AROM) using an oil overlay in an open microscale cell culture system is presented. Using this method, the oxygen microenvironment is dynamically regulated via the supply-demand balance of the system. Numerical simulation and experimental validation of oxygen transport within multi-liquid-phase, microscale culture systems involving a variety of cell types, including mammalian, fungal, and bacterial cells are presented. Finally, AROM is applied to establish a coculture between cells with disparate oxygen demands-primary intestinal epithelial cells (oxygen consuming) and Bacteroides uniformis (an anaerobic species prevalent in the human gut).
Antimicrobial susceptibility testing (AST) remains the cornerstone of effective antimicrobial selection and optimization in patients. Despite recent advances in rapid pathogen identification and resistance marker detection with molecular diagnostics, phenotypic AST methods remain relatively unchanged over the last few decades. Guided by the principles of microfluidics, we describe the application of a multi-liquid-phase microfluidic system, named under-oil open microfluidic systems (UOMS) to achieve a rapid phenotypic AST. UOMS provides a next-generation solution for AST (UOMS-AST) by implementing and recording a pathogen antimicrobial activity in micro-volume testing units under an oil overlay with label-free, single-cell resolution optical access. UOMS-AST can accurately and rapidly determine antimicrobial activity from nominal sample/bacterial cells in a system aligned with clinical laboratory standards. Further, we combine UOMS-AST with cloud lab data analytic techniques for real-time image analysis and report generation to provide a rapid (i.e., <4 h) sample-to-answer turnaround time, shedding light on its utility as a next-generation phenotypic AST platform for clinical application.
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